Comparison of the clinical characteristics of SARS-CoV-2 Delta (B.1.617.2) and Omicron (B.1.1.529) infected patients from a single hospitalist service

Background While existing evidence suggests less severe clinical manifestations and lower mortality are associated with the Omicron variant as compared to the Delta variant. However, these studies fail to control for differences in health systems facilities and providers. By comparing patients hospitalized on a single medical service during the Delta and Omicron surges we were able to conduct a more accurate comparison of the two varaints’ clinical manifestations and outcomes. Methods We conducted a prospective study of 364 Omicron (BA.1) infected patients on a single hospitalist service and compared these findings to a retrospective analysis of 241 Delta variant infected patients managed on the same service. We examined differences in symptoms, laboratory measures, and clinical severity between the two variants and assessed potential risk drivers for case mortality. Findings Patients infected with Omicron were older and had more underlying medical conditions increasing their risk of death. Although they were less severely ill and required less supplemental oxygen and dexamethasone, in-hospital mortality was similar to Delta cases, 7.14% vs. 4.98% for Delta (q-value = 0.38). Patients older than 60 years or with immunocompromised conditions had much higher risk of death during hospitalization, with estimated odds ratios of 17.46 (95% CI: 5.05, 110.51) and 2.80 (1.03, 7.08) respectively. Neither vaccine history nor variant type played a significant role in case fatality. The Rothman score, NEWS-2 score, level of neutrophils, level of care, age, and creatinine level at admission were highly predictive of in-hospital death. Interpretation In hospitalized patients, the Omicron variant is less virulent than the Delta variant but is associated with a comparable mortality. Clinical and laboratory features at admission are informative about the risk of death. Supplementary Information The online version contains supplementary material available at 10.1186/s12879-023-08714-x.

Table S1.Frequency (percent) for qualitative and median (IQR) for quantitative characteristics of unvaccinated patients infected by Omicron or Delta who were admitted to the University of Florida Health System during Jun.2021 -Feb.2022.q-values£0.05(bolded) control the false discovery rate to be £5%.

Criteria for Hospital Admission
Positive SARS-CoV-2 positive RTPCR AND an increased respiratory rate (≥30 breaths per min) OR hypoxia with oxygen saturation ≤ 94% on RA or a decrease in saturation to < 90% with ambulation.OR In the absence of the above findings, patients with a high risk of a poor outcome:

WHO classification of illness severity (12/2022, included on the Red Cap entry form below)
Mild Illness: Individuals who have any of the various signs and symptoms of COVID-19 but who do not have shortness of breath, dyspnea, or abnormal chest imaging.Moderate Illness: Individuals who show evidence of lower respiratory disease during clinical assessment or imaging and who have an oxygen saturation (SpO2) ≥94% on room air at sea level.Severe Illness: Individuals who have SpO2 < 94% on room air at sea level, a ratio of arterial partial pressure of oxygen to fraction of inspired oxygen (PaO2/FiO2) < 300 mm Hg, respiratory frequency >30 breaths/min, or lung infiltrates >50%.Critical Illness: Individuals who have respiratory failure, septic shock, and/or multiple organ dysfunction.

Criteria for assigning the cause of death to be COVID-19
• The reason for admission was determined to be COVID-19 • Laboratory data indicated active SARS-COV-2 infection -oxygen saturation < 94%, , low lymphocyte count, elevated CRP • Infiltrate on CXR or pulmonary CT scan.
• COVID-19 listed as a primary diagnosis on the discharge summary.
• No other underlying illness was likely to be the cause of death.

Additional Details on X-Boost Machine Learning Risk of Death Model
A tree complexity of five, a learning rate of 0.005 and a bagging fraction of 75% were used based on our previous research.A 10-fold cross validation was used to identify the optimal number of trees using the gbm.step function.To improve predictive power, we randomly divided the data set into 75% training set and 25% test set for 100 times to form 100 permutation data sets and fitted BRT to each data set.The outputs of the BRT models, both marginal effect curves and relative contributions of predictors, were summarized by median and inter-quartile range over the 100 permutation data sets.For each model, the relative contribution was calculated based on how many times a predictor was chosen for splitting and how much each split improved the objective function, averaging over all model-included trees.Modeling analyses was performed for all deaths and COVID-19-attributable deaths separately.In addition, a significant number of deaths occurred after discharge, and modeling analyses was also stratified by whether deaths within 30 days of discharge were included as death events.Deaths beyond 30 days of discharge were considered non-death events.

Figure S2 :
Figure S2: Timeline of patients admitted during June 2021 -February 2022 by date of first positive PCR (A) and date of admission (B).Patients detected after November 2021 and confirmed by whole genome sequencing were colored in blue for Omicron and orange for Delta.Patients detected before November 2021 were suspected to be Delta-infected and also colored in orange.Patients who were detected after November 2021 but had no whole genome sequence were colored in grey.The vertical solid line marks January 8, 2022, after which we assume all patients without whole genome sequence were Omicron-infected.

Figure S3 :
Figure S3: Average (solid red) and (2.5%, 95%) quantiles of response curves over the 100 random data splits for factors with relative importance ³7 based on the XGBOOST model for predicting in-hospital death of all causes: (A) Initial Rothman Score, (B) NEW2 Score, (C) log(Creatinine), (D) Neutrophils, (E) Level of Care, and (F) Age group.In (A)-(D), the background histograms show the distribution of the factor's values in the data.
Index.Look at the scores Improved the day of positive RTPCR test or if the test was No significant change before admission on the day of admission, and look at Worsened the scores the day of discharge.If the discharge score is 10 points or higher than the admission score = Improved.10 points lower = Worsened.If no < 10 point change = No significant change.(Access RI trend graph by opening the chart through Patient Station) Rothman Index at the time RTPCR positive (

Table S2 . Logistic regression analysis of deaths among hospitalized patients infected by the Omicron and Delta Variants, where Omicron patients are restricted those who were sequence-confirmed. Showing Odds Ratios † (95% confidence intervals). Variables COVID-attributed Deaths All-Cause Deaths In-hospital In-hospital + ≤30 days of discharge In-hospital In-hospital + ≤30 days of discharge
† Odds Ratios stratified by whether death was attributed to COVID-19 and whether death occurred during hospitalization.Results with statistical significance are bolded.‡Positive RT-PCR test before admission vs. after admission correspond to days from RT-PCR+ to admission >0 vs. £0.

Table S3 . Odds ratios (95% confidence interval) based on logistic regression of death outcome among hospitalized patients infected with delta or omicron, stratified by whether death was attributed to COVID-19 and whether death occurred during hospitalization. Backward variable selection was conducted for each outcome separately. Variant and vaccination status were forced to stay in each model.
Positive RT-PCR test before admission vs. after admission correspond to days from RT-PCR+ to admission >0 vs. £0. *